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paddlefold

README.md

PaddleFold

Reproduction of AlphaFold 2 with PaddlePaddle.

PaddleFold currently provides a PaddlePaddle implementation of the AlphaFold inference pipeline, and reproduces all of the features of the original open source inference code (v2.0.1) including recycle and ensembling.

Trainable PaddleFold is coming soon.

Installation

PaddleFold depends on PaddlePaddle. Python dependencies available through pip is provided in requirements.txt. PaddleFold also depends on openmm==7.5.1 and pdbfixer, which are only available via conda. For producing multiple sequence alignments, kalign, the HH-suite and jackhmmer are also needed. The download scripts require aria2c.

We provide a script setup_env that setup a conda environment and installs all dependencies. Run:

sh setup_env
conda activate paddlefold # activate the conda environment

You can change the name of the environment and CUDA version in setup_env.

Usage

In order to run PaddleFold, the genetic databases and model parameters are required.

You can use a script scripts/download_all_data.sh, which is the same as the original AlphaFold that can be used to download and set up all databases and model parameters:

  • Default:

    scripts/download_all_data.sh <DOWNLOAD_DIR>

    will download the full databases. The total download size for the full databases is around 415 GB and the total size when unzipped is 2.2 TB.

  • With reduced_dbs:

    scripts/download_all_data.sh <DOWNLOAD_DIR> reduced_dbs

    will download a reduced version of the databases to be used with the reduced_dbs preset. The total download size for the reduced databases is around 190 GB and the total size when unzipped is around 530 GB.

Running PaddleFold for inference

To run inference on a sequence or multiple sequences using a set of DeepMind's pretrained parameters, run e.g.:

fasta_file="target.fasta" # path to the target protein
model_name="model_1" # the alphafold model name
DATA_DIR="data" # path to the databases
OUTPUT_DIR="paddlefold_output" # path to save the outputs

python3 run_paddlefold.py \
  --fasta_paths=${fasta_file} \
  --data_dir=${DATA_DIR} \
  --small_bfd_database_path=${DATA_DIR}/small_bfd/bfd-first_non_consensus_sequences.fasta \
  --uniref90_database_path=${DATA_DIR}/uniref90/uniref90.fasta \
  --mgnify_database_path=${DATA_DIR}/mgnify/mgy_clusters_2018_12.fa \
  --pdb70_database_path=${DATA_DIR}/pdb70/pdb70 \
  --template_mmcif_dir=${DATA_DIR}/pdb_mmcif/mmcif_files \
  --obsolete_pdbs_path=${DATA_DIR}/pdb_mmcif/obsolete.dat \
  --max_template_date=2020-05-14 \
  --model_names=${model_name} \
  --output_dir=${OUTPUT_DIR} \
  --preset='reduced_dbs' \
  --jackhmmer_binary_path /opt/conda/envs/paddlefold/bin/jackhmmer \
  --hhblits_binary_path /opt/conda/envs/paddlefold/bin/hhblits \
  --hhsearch_binary_path /opt/conda/envs/paddlefold/bin/hhsearch \
  --kalign_binary_path /opt/conda/envs/paddlefold/bin/kalign \
  --random_seed=0

You can use python3 run_paddlefold.py -h to find the description of the arguments.

We retain the same outputs as AlphaFold. We copy the AlphaFold's descriptions here.

The outputs will be in a subfolder of output_dir. They include the computed MSAs, unrelaxed structures, relaxed structures, ranked structures, raw model outputs, prediction metadata, and section timings. The output_dir directory will have the following structure:

<target_name>/
    features.pkl
    ranked_{0,1,2,3,4}.pdb
    ranking_debug.json
    relaxed_model_{1,2,3,4,5}.pdb
    result_model_{1,2,3,4,5}.pkl
    timings.json
    unrelaxed_model_{1,2,3,4,5}.pdb
    msas/
        bfd_uniclust_hits.a3m
        mgnify_hits.sto
        uniref90_hits.sto

The contents of each output file are as follows:

  • features.pkl – A pickle file containing the input feature NumPy arrays used by the models to produce the structures.

  • unrelaxed_model_*.pdb – A PDB format text file containing the predicted structure, exactly as outputted by the model.

  • relaxed_model_*.pdb – A PDB format text file containing the predicted structure, after performing an Amber relaxation procedure on the unrelaxed structure prediction (see Jumper et al. 2021, Suppl. Methods 1.8.6 for details).

  • ranked_*.pdb – A PDB format text file containing the relaxed predicted structures, after reordering by model confidence. Here ranked_0.pdb should contain the prediction with the highest confidence, and ranked_4.pdb the prediction with the lowest confidence. To rank model confidence, we use predicted LDDT (pLDDT) scores (see Jumper et al. 2021, Suppl. Methods 1.9.6 for details).

  • ranking_debug.json – A JSON format text file containing the pLDDT values used to perform the model ranking, and a mapping back to the original model names.

  • timings.json – A JSON format text file containing the times taken to run each section of the AlphaFold pipeline.

  • msas/ - A directory containing the files describing the various genetic tool hits that were used to construct the input MSA.

  • result_model_*.pkl – A pickle file containing a nested dictionary of the various NumPy arrays directly produced by the model. In addition to the output of the structure module, this includes auxiliary outputs such as:

    • Distograms (distogram/logits contains a NumPy array of shape [N_res, N_res, N_bins] and distogram/bin_edges contains the definition of the bins).
    • Per-residue pLDDT scores (plddt contains a NumPy array of shape [N_res] with the range of possible values from 0 to 100, where 100 means most confident). This can serve to identify sequence regions predicted with high confidence or as an overall per-target confidence score when averaged across residues.
    • Present only if using pTM models: predicted TM-score (ptm field contains a scalar). As a predictor of a global superposition metric, this score is designed to also assess whether the model is confident in the overall domain packing.
    • Present only if using pTM models: predicted pairwise aligned errors (predicted_aligned_error contains a NumPy array of shape [N_res, N_res] with the range of possible values from 0 to max_predicted_aligned_error, where 0 means most confident). This can serve for a visualisation of domain packing confidence within the structure.

The pLDDT confidence measure is stored in the B-factor field of the output PDB files (although unlike a B-factor, higher pLDDT is better, so care must be taken when using for tasks such as molecular replacement).

Copyright

PaddleFold code is licensed under the Apache 2.0 License, which is same as AlphaFold. However, we use the AlphaFold parameters pretrained by DeepMind, which are made available for non-commercial use only under the terms of the CC BY-NC 4.0 license.